...cost

Such as the Sony EVI-D30 and the Cannon VCC1.

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...system.

The parameter $\alpha$expresses the trade-off between the two types of recognition errors, false alarms and misses; for the all the results presented in this paper we have taken a conservative approach and set $\alpha=10$. This is somewhat similar to the idea of disproportionately penalizing perceptually aliased states in Whitehead's Lion algorithm [27].

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...discriminable

In these examples observation differences were determined using an ad-hoc threshold $\theta$ (we set $\theta=2$) on the variance normalized average pixel differences between images in the two observations.

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...representation.

Note that instance-based Q-learning does not assume any knowledge of the set of states $\cal S$, or the likelihood functions $Tr(\cdot)$, $Ob(\cdot)$.

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...zero.

Maximum utility can never be negative, since the null action will always lead to zero or greater utility (it can be repeated infinitely with zero reward).

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...20.0

The image range was [0,255].

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...labels.

Ideally, this would eventually lead to a behavior representation that integrated discrete and continuous control, such as in [15].

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...selection.

When merging two sequences we perform a weighted average of utility, setting the weight of the merged sequence to be the sum of the individual weights (which are initialized to 1.) We do modify the Nearest Sequence Match algorithm such that this weight value is considered when averaging utility and computing match length histograms.

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...level.

Policies can also be trained simultaneously for multiple goals, using the approach presented in [11]. For the purposes of pruning and conversion to representation space, however, each learned policy is considered separate.

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